Abstract--Recently, there has been a growing demand for the deployment of Explainable Artificial Intelligence (XAI) algorithms in real-world applications. However, traditional XAI methods typically suffer from a high computational complexity problem, which discourages the deployment of real-time systems to meet the time-demanding requirements of real-world scenarios. Although many approaches have been proposed to improve the efficiency of XAI methods, a comprehensive understanding of the achievements and challenges is still needed. To this end, in this paper we provide a review of efficient XAI. The efficient non-amortized methods focus on data-centric or model-centric acceleration upon each individual instance. In contrast, amortized methods focus on learning a unified distribution of model explanations, following the predictive, generative, or reinforcement frameworks, to rapidly derive multiple model explanations. We also analyze the limitations of an efficient XAI pipeline from the perspectives of the training phase, the deployment phase, and the use scenarios. Finally, we summarize the challenges of deploying XAI acceleration methods to real-world scenarios, overcoming the trade-off between faithfulness and efficiency, and the selection of different acceleration methods. Despite the advancements in ML, providing instance requires a unique explainer during the derivation transparency in the models, particularly in deep neural of the explanation. In addition, the local explanation suffers networks (DNNs), remains a substantial challenge. The lack from extensive computational conditions due to the pending of transparency can lead to mistrust and skepticism of ML amounts of tested instances, where each instance requires model predictions, such as the block-box driving decisions massive permutation times to complete the importance score made by autopilots.

Several explainable AI methods allow a Machine Learning user to get insights on the classification process of a black-box model in the form of local linear explanations. With such information, the user can judge which features are locally relevant for the classification outcome, and get an understanding of how the model reasons. Standard supervised learning processes are purely driven by the original features and target labels, without any feedback loop informed by the local relevance of the features identified by the post-hoc explanations. In this paper, we exploit this newly obtained information to design a feature engineering phase, where we combine explanations with feature values. To do so, we develop two different strategies, named Iterative Dataset Weighting and Targeted Replacement Values, which generate streamlined models that better mimic the explanation process presented to the user. We show how these streamlined models compare to the original black-box classifiers, in terms of accuracy and compactness of the newly produced explanations.

Algorithmic Recourse refers to the capability of an algorithm to provide explanations and make recommendations in response to an appeal or challenge raised by an individual who has been affected negatively by its decision Wachter et al. (2017); Ustun et al. (2019); Karimi et al. (2020); Venkatasubramanian and Alfano (2020). This concept is particularly important in areas such as finance, healthcare, and criminal justice where decisions made by algorithms can have significant impacts on people's lives Chou et al. (2022). Recently, there has been an explosion of proposals for counterfactual explainers in the emerging field of algorithmic recourse Guidotti (2022); Stepin et al. (2021); Karimi et al. (2021); Verma et al. (2020). Ensuring fairness and robustness in algorithmic decision-making processes is crucial to guarantee fair and just outcomes for all involved. In the context of algorithmic recourse, robustness refers to the ability of an algorithm to withstand unreliability, manipulation, or deception by malicious actors, while still providing fair and accurate recourse recommendations Slack et al. (2021); Upadhyay et al. (2021); Dominguez-Olmedo et al. (2022); Pawelczyk et al. (2022). There are four types of unreliabilities in counterfactual explanations Mishra et al. (2021): Robustness to input perturbations: Examining recourse behavior in response to slight input changes while the classifier remains unchanged Dominguez-Olmedo et al. (2022).

There are many advantages of grasping how an AI-enabled system has arrived at a particular output. Explainability can help developers ensure that their algorithms are working as expected and meeting regulatory standards. It also makes it possible for those affected by an AI decision to challenge or change that outcome. But before we hold AI machines to such high levels of explainability, there is a crucial question: How good are humans in explaining themselves? Riding a bicycle is considered a very simple human action.

Proponents of explainable AI have often argued that it constitutes an essential path towards algorithmic fairness. Prior works examining these claims have primarily evaluated explanations based on their effects on humans' perceptions, but there is scant research on the relationship between explanations and distributive fairness of AI-assisted decisions. In this paper, we conduct an empirical study to examine the relationship between feature-based explanations and distributive fairness, mediated by human perceptions and reliance on AI recommendations. Our findings show that explanations influence fairness perceptions, which, in turn, relate to humans' tendency to adhere to AI recommendations. However, our findings suggest that such explanations do not enable humans to discern correct and wrong AI recommendations. Instead, we show that they may affect reliance irrespective of the correctness of AI recommendations. Depending on which features an explanation highlights, this can foster or hinder distributive fairness: when explanations highlight features that are task-irrelevant and evidently associated with the sensitive attribute, this prompts overrides that counter stereotype-aligned AI recommendations. Meanwhile, if explanations appear task-relevant, this induces reliance behavior that reinforces stereotype-aligned errors. These results show that feature-based explanations are not a reliable mechanism to improve distributive fairness, as their ability to do so relies on a human-in-the-loop operationalization of the flawed notion of "fairness through unawareness". Finally, our study design provides a blueprint to evaluate the suitability of other explanations as pathways towards improved distributive fairness of AI-assisted decisions.

A gradual semantics takes a weighted argumentation framework as input and outputs a final acceptability degree for each argument, with different semantics performing the computation in different manners. In this work, we consider the problem of attack inference. That is, given a gradual semantics, a set of arguments with associated initial weights, and the final desirable acceptability degrees associated with each argument, we seek to determine whether there is a set of attacks on those arguments such that we can obtain these acceptability degrees. The main contribution of our work is to demonstrate that the associated decision problem, i.e., whether a set of attacks can exist which allows the final acceptability degrees to occur for given initial weights, is NP-complete for the weighted h-categoriser and cardinality-based semantics, and is polynomial for the weighted max-based semantics, even for the complete version of the problem (where all initial weights and final acceptability degrees are known). We then briefly discuss how this decision problem can be modified to find the attacks themselves and conclude by examining the partial problem where not all initial weights or final acceptability degrees may be known.

With the advent of 5G commercialization, the need for more reliable, faster, and intelligent telecommunication systems are envisaged for the next generation beyond 5G (B5G) radio access technologies. Artificial Intelligence (AI) and Machine Learning (ML) are not just immensely popular in the service layer applications but also have been proposed as essential enablers in many aspects of B5G networks, from IoT devices and edge computing to cloud-based infrastructures. However, existing B5G ML-security surveys tend to place more emphasis on AI/ML model performance and accuracy than on the models' accountability and trustworthiness. In contrast, this paper explores the potential of Explainable AI (XAI) methods, which would allow B5G stakeholders to inspect intelligent black-box systems used to secure B5G networks. The goal of using XAI in the security domain of B5G is to allow the decision-making processes of the ML-based security systems to be transparent and comprehensible to B5G stakeholders making the systems accountable for automated actions. In every facet of the forthcoming B5G era, including B5G technologies such as RAN, zero-touch network management, E2E slicing, this survey emphasizes the role of XAI in them and the use cases that the general users would ultimately enjoy. Furthermore, we presented the lessons learned from recent efforts and future research directions on top of the currently conducted projects involving XAI.

Recommender system practitioners are facing increasing pressure to explain recommendations. We explore how to explain recommendations using counterfactual logic, i.e. "Had you not interacted with the following items, we would not recommend it." Compared to the traditional explanation logic, counterfactual explanations are easier to understand, more technically verifiable, and more informative in terms of giving users control over recommendations. The major challenge of generating such explanations is the computational cost because it requires repeatedly retraining the models to obtain the effect on a recommendation caused by the absence of user history. We propose a learning-based framework to generate counterfactual explanations. The key idea is to train a surrogate model to learn the effect of removing a subset of user history on the recommendation. To this end, we first artificially simulate the counterfactual outcomes on the recommendation after deleting subsets of history. Then we train a surrogate model to learn the mapping between a history deletion and the corresponding change of the recommendation caused by the deletion. Finally, to generate an explanation, we find the history subset predicted by the surrogate model that is most likely to remove the recommendation. Through offline experiments and online user studies, we show our method, compared to baselines, can generate explanations that are more counterfactually valid and more satisfactory considered by users.

The increasing complexity of AI systems has led to the growth of the field of explainable AI (XAI), which aims to provide explanations and justifications for the outputs of AI algorithms. These methods mainly focus on feature importance and identifying changes that can be made to achieve a desired outcome. Researchers have identified desired properties for XAI methods, such as plausibility, sparsity, causality, low run-time, etc. The objective of this study is to conduct a review of existing XAI research and present a classification of XAI methods. The study also aims to connect XAI users with the appropriate method and relate desired properties to current XAI approaches. The outcome of this study will be a clear strategy that outlines how to choose the right XAI method for a particular goal and user and provide a personalized explanation for users.

Open-textured terms in written rules are typically settled through interpretive argumentation. Ongoing work has attempted to catalogue the schemes used in such interpretive argumentation. But how can the use of these schemes affect the way in which people actually use and reason over the proper interpretations of open-textured terms? Using the interpretive argument-eliciting game Aporia as our framework, we carried out an empirical study to answer this question. Differing from previous work, we did not allow participants to argue for interpretations arbitrarily, but to only use arguments that fit with a given set of interpretive argument templates. Finally, we analyze the results captured by this new dataset, specifically focusing on practical implications for the development of interpretation-capable artificial reasoners.